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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Published on: February 9, 2017

Random walks on temporal networks.

Michele Starnini1, Andrea Baronchelli, Alain Barrat

  • 1Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Barcelona, Spain.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 26, 2012
PubMed
Summary
This summary is machine-generated.

Random walks on dynamic networks are slower than on static ones, especially due to temporal correlations. This research explores network dynamics and their impact on processes like information diffusion.

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Area of Science:

  • Network Science
  • Complex Systems
  • Statistical Physics

Background:

  • Networks naturally evolve over time, with nodes and connections changing dynamically.
  • Understanding processes on these evolving networks is crucial but remains challenging.
  • Temporal network analysis is an emerging field with many open questions.

Purpose of the Study:

  • To investigate random walk dynamics on empirical temporal networks.
  • To identify key network properties influencing processes on evolving networks.
  • To understand the impact of temporal correlations and network duration on random walks.

Main Methods:

  • Analysis of empirical temporal contact networks.
  • Characterization of fundamental quantities impacting network processes.
  • Introduction of randomizing strategies to isolate network property effects.
  • Comparison of random walk behavior on temporal vs. aggregate projected networks.

Main Results:

  • Random walks are demonstrably slower on temporal networks compared to their aggregate projections, even with time rescaling.
  • Temporal correlations between consecutive contacts significantly influence random walk exploration.
  • The limited duration of real-world dynamic networks has notable consequences for processes.

Conclusions:

  • Temporal correlations are a critical factor in random walk dynamics on evolving networks.
  • The findings offer insights into complex dynamics on time-varying network structures.
  • This study provides a foundation for understanding information diffusion and other processes on dynamic networks.